Metadata-Version: 2.1
Name: QCNN
Version: 1.0.1
Summary: Deep Neural Network module for classification/regression
Home-page: UNKNOWN
License: UNKNOWN
Description: # Description  
        
        Quantcore's implementation of a deep neural network. Used for classification and regression.
        
        #Usage
        
        The class takes a few requirements, # input nodes,  # hidden nodes, # layers, # output nodes, # learning rate, # number of epochs, # output type (specified with r for regression, c for classification), # train test split = 0.8 (percentage you would like to train on), # test = True (turn it off if you don't want to test), split = True (turn it off if you don't want the data split)
        
        The train function takes one input, which is a DATAFRAME with the inputs and the outputs on the right side, e.g.
        ```
        inputs = dataframe[:-1]
        outputs = dataframe[-1]
        ```
        Do not separate inputs from outputs, just have the outputs as the righter most column in your df
        
        The call to train the function is class_.train(input)
        
        The call to test is class_.feed_forward(input)
        
        #Installation
        
        ```
        pip install QCNN
        ```
        
        #Sample Code
        ```
        
        from QCNN import NeuralNetwork
        import pandas as pd
        
        
        nn= NeuralNetwork(2,2,3,1,.1,100000, train_test_split =1, split=False)
        
        data_dict = {0:{'input1':0,'input2':0,'output':0},
                    1:{'input1':1,'input2':0,'output':1},
                    2:{'input1':0,'input2':1,'output':1},
                    3:{'input1':1,'input2':1,'output':0}
                    }
        
        df = pd.DataFrame.from_dict(data_dict, orient = 'index')
        
        
        
        nn.train(df)
        
        ```
        
Platform: UNKNOWN
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
